Assessing Digital Maturity in Chile’s Mining Cluster: A Multi-Dimensional Model-Based Approach

Abstract

As digitalization reshapes industrial ecosystems, small and medium-sized enterprises (SMEs) in resource-based economies face growing pressure to adapt. This study examines the digital maturity of supplier firms within Chile’s Antofagasta mining cluster, a region that plays a central role in national productivity. A structured survey was conducted with 83 companies, using a ten-dimensional model to assess key areas such as data management, processes, personnel, and technology use. Results show that the average maturity level is 2.5 on a five-point scale, placing most firms at an early stage of digital transformation. While data-related capabilities scored relatively high, critical gaps persist in automation, robotics, and cybersecurity. Company size was moderately correlated with digital maturity, but no consistent relationship was observed with revenue growth. Although most firms acknowledge the relevance of digital technologies, few have formal plans or strategies in place. These findings reveal a structural lag that limits the potential of SMEs to engage fully with Industry 4.0, underscoring the need for tailored support policies and collaborative development initiatives in the mining sector.

1. Introduction

In the context of global industrial evolution, digital transformation has emerged as a defining factor in the competitiveness and sustainability of enterprises [1,2]. Digital transformation has become a defining factor, and recent surveys indicate that more than 60% of small and medium enterprises (SMEs) in emerging economies have launched digital initiatives [3]. Although multinational corporations often lead digital innovation, small and medium enterprises (SMEs) face barriers related to resource constraints, access to knowledge, and organizational readiness [4]. These limitations are particularly acute in resource-dependent economies, where technological asymmetries between firms can reinforce structural inequalities [5].

In Chile, the mining sector currently contributes approximately 14% of GDP (13.6% in 2022 according to the U.S. International Trade Administration) [6]. The Antofagasta Region plays a central role in this structure, concentrating a significant share of mining operations and serving as a hub for industrial collaboration. Within this ecosystem, supplier companies (predominantly SMEs) represent a strategic lever for innovation, diversification, and employment. However, their capacity to adopt digital tools and transition to Industry 4.0 remains uneven [7].

Understanding the level of digital maturity among supplier firms is crucial to guide effective policy, support industrial upgrading, and facilitate the integration of SMEs into global digital value chains. Maturity models provide a structured approach to assess technological capabilities, internal processes, and strategic alignment of firms with digital transformation objectives. In this study, we adopt the ten-dimensional model initially proposed by Appelfeller and Feldmann (2018) [8], which evaluates key organizational areas such as data management, automation, customer relationships, and cybersecurity. This is consistent with recent reviews that identify various methodological approaches for small and medium enterprises, including Viloria-Núñez et al. (2022) [9] and Williams et al. (2024) [10].

Beyond Appelfeller and Feldmann, several frameworks have been applied to SMEs in Europe and Latin America. For example, Stich et al. [11] identified measures for successful digital transformation, while Oliveri et al. [12] highlighted the role of organizational mindset in enabling digital transitions. These studies emphasize that methodological diversity and contextual adaptation are crucial when assessing the digital readiness of small businesses.

Figure 1 presents the average digital maturity scores in the ten dimensions included in the adapted model. The results show a heterogeneous profile: while some areas, such as data management and personnel capabilities, reach moderate values, others, such as automation and cybersecurity, remain underdeveloped. This figure provides a general overview of the maturity distribution and serves as a baseline for more detailed comparisons in subsequent analyses.

Based on a structured survey applied to 83 companies in the Antofagasta mining cluster, we analyze their current level of digital maturity, identify strengths and weaknesses, and explore statistical relationships between digital development and organizational characteristics. This study offers empirical evidence on an underexplored segment of the Chilean economy. It provides actionable insights for public and private stakeholders seeking to foster a more inclusive and resilient digital transition. The results are presented descriptively and later interpreted in a dedicated Discussion section, which anticipates structural asymmetries and the role of ecosystems.

2. Methods

This study used a descriptive cross-sectional approach to assess the level of digital maturity among supplier firms in the Antofagasta mining cluster. The methodology combined the application of a structured diagnostic instrument with quantitative analysis using descriptive statistics and correlation measures.

2.1. Survey Design and Model Adaptation

While grounded in the Appelfeller and Feldmann framework [8], the present study introduces sector-specific adaptations, including supplier connectivity, workforce skills, and mining-specific automation. The model comprises ten key dimensions reflecting organizational, technological, and operational aspects of digitalization.

Table 1. Dimensions assessed in the adapted digital maturity model.

Dimension	No. of Items
Business Processes (e.g., logistics, procurement, HR)	1 (covering 8 subprocesses)
Supplier Connectivity	2
Personnel Capabilities	2
Data Management	3
Product and Service Digitalization	3
Information Systems	2
Business Model Adaptation	3
System Integration/Interconnection	2
Customer Relationship Management	3
Automation and Robotics	1

summarizes these dimensions and the number of items used to assess them, ranging from business processes and data management to customer relationships and automation. This structure highlights the multi-dimensional nature of digital maturity while also illustrating how sector-specific needs are incorporated into the diagnostic instrument.

The structure presented in

Table 2. Structure of the digital maturity survey instrument.

Section	Dimension	Items	Example Indicator
Organizational Processes	Business Processes	1 (8 sub-items)	Use of ERP in logistics, HR, and procurement
Technological Systems	Information Systems	2	Existence of integrated platforms
	Automation and Robotics	1	Use of automated machinery or robotics
	Interconnection	2	System interoperability with suppliers/clients
Digital Resources	Data Management	3	Real-time data capture and analytics
	Cybersecurity	1	Implementation of security protocols
Customer Interface	Customer Relationships	3	Digital channels for sales and service
Strategic Layer	Business Model Adaptation	3	Revenue from digital-enabled services
Workforce	Personnel	2	Digital tools used by staff
Partner Connectivity	Supplier Connectivity	2	Use of digital platforms with providers

reflects our adaptation: dimensions are grouped into five thematic sections (Organizational Processes, Technological Systems, Digital Resources, Customer Interface, and Strategic Layer), aligning the original model with mining-sector terminology. The column ‘Items’ refers to individual Likert-scale questions.

Similar sector-specific adaptations have been proposed for industrial firms in other countries [13], emphasizing flexibility in the design of maturity models.

2.2. Sampling and Data Collection

The target population consisted of approximately 2000 SMEs identified as active suppliers to the mining sector in the Antofagasta region. Using a 10% margin of error, a confidence level of 93%, and an assumed response heterogeneity of 50%, the required sample size was calculated in 83 firms. Data were collected through an online survey distributed with the support of local business associations and development agencies. The survey was conducted between March and July 2020. The sample size of 83 firms was calculated using a 10% margin of error, a 93% confidence level, and 50% heterogeneity, based on around 2000 registered suppliers.

2.3. Data Analysis

The responses were analyzed using SPSS Statistics 27 software. Descriptive statistics were used to assess maturity scores by dimension, and the Pearson correlation coefficient was applied to test associations between digital maturity, company size (number of employees), and revenue growth over the past three years. The respondents included general managers (54.7%), company owners (11.6%), and other decision makers.

All responses were anonymized and reviewed for completeness and consistency before analysis. The study was carried out according to ethical guidelines for non-invasive business research [14]. Figure 2 provides a graphical synthesis of this methodological flow, illustrating the logical progression from model adaptation to the statistical analysis of collected data.

3. Dataset Structure

The dataset resulting from this study is structured around the responses of 83 firms, each evaluated in ten dimensions of digital maturity. Each record corresponds to a unique firm and includes metadata such as company size, sector of activity, years of operation, and self-reported revenue trend.

The dataset is organized as a flat table in CSV format, where rows represent firms and columns represent maturity scores for each dimension and related metadata. Each item in the diagnostic instrument is stored as an individual variable, allowing for both dimension-level and item-level analysis. All values are numerical, on a scale from 1 (nonexistent) to 5 (fully implemented), and categorical fields (such as sector or respondent role) are encoded using clear, standardized labels.

The dataset is structured into three main groups of variables. The first group corresponds to the firm metadata, which includes the company identifier, size category, number of employees, sector of activity, years of operation, and respondent role. The second group contains maturity scores, both aggregated values for the ten dimensions and disaggregated scores for individual items. The third group consists of outcome variables, such as the self-reported revenue trend (increase, stable, or decrease), the existence of digital transformation plans, and the use of ERP or CRM systems. For example, a medium-sized engineering supplier with 120 employees and 15 years of activity reported scores of 4 in Data Management, 3 in Customer Relationships, and 1 in Automation and Robotics, together with a self-declared stable revenue trend. This structure not only supports the reproducibility of the original analysis but also facilitates extensions such as clustering, benchmarking, or longitudinal tracking. In addition, the dataset adheres to the FAIR principles [15], ensuring that it is findable, accessible, interoperable, and reusable.

Figure 3 illustrates the logical architecture of the dataset, highlighting the relationship between firm-level metadata, dimensions of digital maturity, and the corresponding operational items. In the top layer, firm metadata such as identification code, firm size, and economic sector serve as key descriptors linked to dimensions such as “Processes” and “Data.” Outcome variables, including digital growth and the use of ERP/CRM systems, are associated with the “Personnel” dimension, reflecting their impact on human resource management practices. Each maturity dimension encompasses specific evaluation items; for example, “Processes” unfold into operational components like logistics, procurement, and human resources. This structure underpins both descriptive and inferential analyses, enabling a consistent mapping between the conceptual model and collected metrics.

4. Technical Validation and Benchmarking

To ensure data consistency and analytical reliability, multiple validation procedures were implemented. The responses were screened for completeness, and only those with valid entries for all core dimensions were included. Internal consistency was tested using Cronbach’s alpha, which yielded a value of 0.81 throughout the instrument, indicating good internal reliability [16].

A fundamental benchmarking analysis was conducted to compare maturity levels between firm sizes. The companies were categorized as micro, small, medium, or large according to the number of employees, according to the official Chilean classification standards [17]. Figure 4 displays the average maturity scores by firm size.

The results confirm a positive trend between company size and digital maturity, in line with Ghobakhloo (2020) [5] and Mittal et al. (2018) [4], who also reported a positive correlation between firm size and maturity. However, no significant correlation was observed between maturity scores and revenue growth, suggesting that digital transformation efforts may not yet translate into measurable financial performance in this context.

An outlier analysis was performed to identify unusually high or low scores. Firms with consistently low ratings across all dimensions often lacked dedicated IT personnel or formal process documentation. High performers tended to report formal digital strategies and ongoing training programs. These insights inform future segmentation strategies for targeted policy intervention.

Figure 5 focuses on six dimensions of digital maturity that exhibited the most significant variability among the surveyed firms, thus highlighting both areas of relative strength and critical weakness. Data Management emerges as the most developed dimension, with an average score of 3.4, reflecting a moderate capacity to capture, store, and analyze business data. Personnel and Business Model follow with scores around 3.0, indicating incipient yet uneven integration of digital tools into workforce practices and value generation processes. By contrast, Integration, Cybersecurity, and Robotics remain significantly underdeveloped, each scoring below 2.2. These gaps underscore persistent deficiencies in inter-system connectivity, security protocols, and automation capabilities. Unlike Figure 1, which presents an overview of all ten dimensions, this figure isolates the most divergent results to illustrate the structural asymmetries that characterize the digital transformation of SMEs in the mining cluster.

5. Case Study: Regional Application in Antofagasta

To illustrate the practical relevance of the digital maturity assessment, this section presents a regional case study focused on the Antofagasta region, Chile’s central mining hub. The area not only contributes more than 50% of the country’s mining exports, but also serves as a testbed for public–private initiatives aimed at fostering innovation between supplier firms [18].

In collaboration with regional development agencies and mining innovation centers, the digital maturity instrument was deployed across a network of SMEs participating in supplier development programs. Firms were invited to complete the self-assessment as a diagnostic input for internal improvement plans. The exercise revealed substantial heterogeneity in maturity levels, even between firms within the same sector. Although only two companies are described, they illustrate the extremes of the maturity spectrum and informed the design of subsequent training and funding interventions. The case analysis thus provides practical, albeit non-generalizable, insights.

For example, two suppliers of industrial maintenance services, each with similar employee counts, reported contrasting levels of digital integration. Firm A had implemented predictive maintenance systems, ERP software and remote monitoring, achieving an average maturity score of 3.8. In contrast, Firm B relied on manual records and reported a score of 2.1, citing cost barriers and lack of technical expertise. Recent work by Rodríguez et al. [19] confirms that the adoption of technology 4.0 in northern Chile remains fragmented but increasingly strategic.

These findings were used to inform the’ targeted interventions of local actors, including digital training workshops and funding schemes for automation projects. Several companies requested follow-up evaluations to monitor progress over time, indicating strong potential to integrate maturity evaluations into continuous improvement processes.

This case reinforces the importance of customized digital strategies and highlights how regional ecosystems can use structured assessment tools to align SME development with broader innovation goals [20].

Figure 6 sensitizes a sequential pathway for translating diagnostic results into concrete policy actions. The process starts with the identification of maturity gaps that reveal specific weaknesses in the digital capabilities of firms. These findings inform two complementary lines of intervention: digital skills training programs and targeted funding schemes for technological upgrading. Both streams converge in a monitoring stage, where follow-up tools are applied to assess effectiveness and track progress. The final stage involves aligning the insights with regional policy frameworks, ensuring that organizational improvements scale into systemic benefits. Unlike prescriptive models, this framework operates as a set of strategic guidelines, promoting adaptive and evidence-based decision making to support SMEs in the Antofagasta mining cluster.

6. Usage Notes

The dataset and framework can be applied in multiple concrete contexts. In the industrial domain, they enable the benchmarking of SMEs in sectors beyond mining, such as manufacturing, where firms face similar challenges in automation, data management, and integration. In education, the model can be incorporated into digital transformation curricula, providing students with a practical tool to evaluate organizational readiness. In the policy arena, the framework can inform regional pilot programs designed to support industrial upgrading and guide targeted interventions. These examples clarify the potential for academic, educational, and policy reuse, making the contribution more actionable and relevant to diverse stakeholders.

• Benchmarking Tools: Public development agencies can replicate the instrument to benchmark digital maturity across industries or regions, providing evidence for targeted programs and funding allocation.
• Curriculum Development: Educational institutions can incorporate anonymized results into digital transformation training modules, allowing students to analyze real-world SME data.
• Policy Evaluation: Policy makers can track the evolution of SME digital maturity over time, assessing the effectiveness of interventions and identifying persistent gaps.
• Inter-firm Comparison: Sector-specific clusters can use the model for internal diagnostics and peer comparisons, fostering horizontal collaboration in capability development.
• Tool Extension: Researchers may adapt or extend the model to evaluate specific technological areas (e.g., AI adoption, cybersecurity readiness) or integrate it into multistakeholder platforms.

The instrument adheres to the FAIR data principles [15], and the survey logic can be easily adapted to other languages and economic sectors. Future work may also include dashboard visualization tools or integration with ERP/CRM platforms to enable automated maturity tracking.

7. Discussion

This section interprets the main findings of the study in relation to the existing literature and the Chilean industrial context. It highlights structural asymmetries, the limits of digital readiness, and the role of ecosystems in supporting SMEs. It also reflects on methodological limitations and proposes future lines of inquiry.

7.1. Uneven Digitalization Across Dimensions

The data reveal a pattern of asymmetric maturity among mining SMEs. While data management and internal processes show moderate progress, areas such as automation and cybersecurity lag significantly. That confirms that digital transformation is not homogeneous and tends to progress in a modular fashion [1]. Similar findings were reported by Arenas et al. [21], who identified critical gaps in automation and cybersecurity among Chilean mining SMEs.

Table 3. Summary of digital maturity levels by dimension.

Dimension	Avg. Score	Interpretation
Data Management	3.4	Moderate capabilities; structured data storage and reporting are common.
Business Model	2.8	Some adaptation to digital services, but weak monetization strategies.
Personnel	2.9	Basic digital literacy among staff, limited training initiatives.
Cybersecurity	1.9	Minimal formal protocols; perceived as a low-priority issue.
Automation/Robotics	1.7	Rare implementation of physical automation technologies.
System Integration	2.1	Fragmented platforms; few firms report full interoperability.

summarizes the average digital maturity scores in six key dimensions assessed in the SMEs surveyed. The highest score was recorded in Data Management (3.4), indicating that most firms have implemented basic capabilities for data storage, access, and reporting. Moderate scores were also observed in Personnel (2.9) and Business Model (2.8), reflecting incipient efforts to improve workforce digital skills and adapt services to new technological contexts, albeit with weak monetization mechanisms.

In contrast, dimensions such as Cybersecurity (1.9), Automation and Robotics (1.7), and System Integration (2.1) received notably low scores. These results suggest that critical enablers of Industry 4.0, such as secure infrastructure, automation, and cross-platform interoperability, remain underdeveloped among mining supplier firms. The low maturity in cybersecurity is particularly relevant, as it reflects a lack of formal protocols and a low prioritization by management. The automation gap confirms a broader trend of limited investment in physical or embedded technologies. In general, the table highlights an asymmetric maturity profile, with information-related capabilities more developed than infrastructural or operational components.

7.2. Structural Constraints on SMEs

The maturity gap is explained in part by structural limitations typical of resource-based SME ecosystems: limited internal technical capacity, dependence on major buyers, and weak planning culture [5]. Even when awareness is high, execution is hindered by resource constraints.

Figure 7 illustrates the primary structural barriers that hinder digital transformation efforts in small and medium enterprises (SMEs). At the core of these limitations is a widespread deficiency in technical skills, which constrains firms’ ability to adopt and sustain digital technologies. This weakness is compounded by the lack of a culture of strategic planning and restricted access to financial capital. These factors interact and ultimately contribute to fragmented ecosystems, where isolated initiatives fail to generate cumulative or systemic impact. The diagram emphasizes the need for holistic interventions that address human, organizational, and financial dimensions in tandem. Similar organizational and infrastructural barriers have been documented among Latin American SMEs, such as limited technical staff and financial constraints [22].

7.3. The Role of Regional Ecosystems

The Antofagasta case suggests that regional innovation ecosystems can partially offset structural barriers through coordination, funding, and training. However, current support mechanisms are fragmented and short-term in scope. More integrated long-term interventions are needed [18]. Morales and Díaz [23] stress the importance of digital workforce competencies as a foundation for sustainable transformation in mining regions.

Despite these efforts, the articulation between academia, government, and industry remains weak. Several of the firms surveyed noted limited awareness of available programs or duplication of efforts between institutions. Public–private partnerships are often transactional rather than strategic, with little follow-up or impact assessment. In this context, the digital transition risks being perceived as a top-down agenda disconnected from local firm realities.

To address these challenges, regional policy design should prioritize systemic coordination, continuity, and capacity building.

Table 4. Roles of ecosystem actors in supporting SME digitalization.

Actor	Observed Role	Recommended Role
Regional Government	Funding isolated programs; limited strategic alignment	Lead long-term policy coordination; integrate with development plans
Universities and CFTs	Sporadic training projects; weak industry links	Provide ongoing reskilling programs; strengthen technology transfer
Development Agencies	Promote instruments reactively	Proactively identify SME needs; tailor support portfolios
Large Mining Firms	Occasional support for suppliers	Serve as anchors for open innovation networks
Business Associations	Information diffusion; low technical engagement	Act as facilitators for peer learning and digital readiness

summarizes the current and ideal roles of key ecosystem actors based on field observations.

7.4. Revenue Growth vs. Maturity: A Weak Link

An unexpected finding was the lack of significant correlation between digital maturity and revenue growth. This raises the possibility that firms are digitalizing in response to institutional pressures or internal needs, rather than immediate economic gains, and confirms recent findings that digital ROI in SMEs may be long-term and indirect [20].

Two structural factors could explain this disconnect. First, the initial phases of digital transformation are associated with sunk costs (equipment, training, and restructuring) that do not immediately translate into revenue. Second, the benefits of digital tools often emerge through increased efficiency or risk mitigation, rather than direct income.

Figure 8 illustrates the average maturity score by self-reported revenue trend. Firms with a decline in revenue reported only marginally lower maturity than those with stable or increasing revenues. That suggests that digitalization alone is not sufficient to overcome broader market or structural constraints.

These findings suggest that operational and process indicators should complement maturity assessments and that policy efforts must align digitalization with broader competitiveness strategies, especially in supply-constrained sectors such as mining services.

7.5. Limitations and Future Research

This study is limited by its cross-sectional design and reliance on self-reported data. Although internal consistency was ensured, causality cannot be inferred. Future research could adopt longitudinal designs, incorporate qualitative methods, or link maturity levels to operational and financial KPIs.

Figure 9 illustrates the distribution of the average digital maturity scores in ten standardized dimensions, disaggregated by firm size (micro, small, and medium/large enterprises). Each axis corresponds to a core capability area, including processes, data management, personnel, customer relationships, cybersecurity, products, information systems, integration, robotics, and business model adaptation. The results show a transparent size-dependent gradient: microenterprises consistently report the lowest levels of maturity, with pronounced weaknesses in robotics and system integration. Small firms achieve moderately higher scores, showing relative strengths in data management and cybersecurity, but still lag in automation-oriented dimensions. Medium and large companies obtain the highest overall values, displaying a more balanced maturity profile, with notable advantages in information systems and product digitalization. This evidence confirms the existence of a structural digital divide shaped by organizational scale, underscoring the importance of tailored support mechanisms and differentiated policy interventions to foster inclusive digital transformation.

8. Conclusions

This study assessed the digital maturity of small and medium enterprises in the Antofagasta mining cluster, Chile, by adapting and operationalizing a ten-dimensional framework. Beyond providing a sectoral diagnosis, the research introduced methodological refinements: adaptation of the Appelfeller and Feldmann model to mining sector realities; explicit grouping of items into organizational, technological and strategic categories; and construction of a structured data set aligned with FAIR principles. These steps enhance the transparency and replicability of the approach, addressing a frequent limitation of prior maturity assessments.

The empirical results confirm that digital transformation remains in its infancy for most firms, with critical gaps in automation, cybersecurity, and system integration. Firm size emerges as a decisive factor: medium and large enterprises consistently outperform micro-firms across all dimensions. However, the absence of a direct link between maturity and revenue growth suggests that performance gains are mediated by long-term processes or contextual variables, rather than producing immediate economic returns.

The findings also underscore the importance of regional ecosystems in enabling digital upgrading. In the case of Antofagasta, coordinated interventions—combining diagnostics, targeted training, funding schemes, and follow-up monitoring—can align SME capabilities with broader innovation and industrial policy objectives. This multilevel perspective positions the model not only as an analytical tool but also as a practical resource for regional development strategies.

In applied terms, the maturity model and the dataset offer multiple opportunities for reuse: benchmarking SMEs across industries, integration into educational curricula on digital transformation, and informing policy pilots. Future work should extend the dataset longitudinally, refine survey items to incorporate emerging technologies (such as AI-driven automation and advanced data analytics), and test causal mechanisms linking maturity dimensions with concrete performance outcomes.

Closing the digital maturity gap is, therefore, more than a technical exercise; it represents a strategic imperative to ensure that SMEs in resource-based economies can participate and benefit from the industrial transformations shaping the future of mining and related sectors.